A recent development in Bayesian optimization is the use of local
optimi...
The visual analytics community has long aimed to understand users better...
Many problems can be viewed as forms of geospatial search aided by aeria...
Local optimization presents a promising approach to expensive,
high-dime...
The visual analytics community has proposed several user modeling algori...
Active search is a setting in adaptive experimental design where we aim ...
Active search is a learning paradigm where we seek to identify as many
m...
We propose the Active Visual Analytics technique (ActiveVA), an augmenta...
Analyzing interaction data provides an opportunity to learn about users,...
Bayesian optimization is a sequential decision making framework for
opti...
The goal of item response theoretic (IRT) models is to provide estimates...
Finite-horizon sequential decision problems arise naturally in many mach...
Graph structured data are abundant in the real world. Among different gr...
We present a novel technique for tailoring Bayesian quadrature (BQ) to m...
Active search is a learning paradigm for actively identifying as many me...
The goal of visual analytics is to create a symbiosis between human and
...
Quadrature is the problem of estimating intractable integrals, a problem...
Autonomous systems can be used to search for sparse signals in a large s...
Determinantal point processes (DPPs) are an important concept in random
...
This paper proposes a novel Gaussian process approach to fault removal i...
Bayesian optimization is a powerful tool for fine-tuning the hyper-param...
We propose a novel sampling framework for inference in probabilistic mod...
We introduce propagation kernels, a general graph-kernel framework for
e...
Many real-world datasets can be represented in the form of a graph whose...
We consider two active binary-classification problems with atypical
obje...